scholarly journals Seasonal Source Apportionment of Heavy Metals and Physicochemical Parameters: A Case Study of Sapanca Lake Watershed

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Asude Ateş ◽  
Hülya Demirel ◽  
Rabia Köklü ◽  
Şenay Çetin Doğruparmak ◽  
Hüseyin Altundağ ◽  
...  
Keyword(s):  
Toxic Metals ◽  
Physicochemical Parameters ◽  
Principal Component ◽  
Pollution Sources ◽  
Statistical Techniques ◽  
Natural Soil ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Pollutant Sources ◽  
Mlr Model

This study was aimed to evaluate the water quality and pollution sources in Sapanca Lake and its tributaries by applying multivariate statistical techniques to physicochemical parameters and toxic metals. For this purpose, the multivariate statistical methods such as principal component analysis (PCA) and absolute principal component score-multiple linear regression (APCS-MLR) model have been employed. It was tried to determine the seasonal pollution sources of physicochemical parameters and toxic metals obtained from 22 different sampling points between the years of 2015 and 2017. PCA was applied to the datasets, and 6 varimax factors describing 84%, 80%, 76%, and 79% of the total variance for each season were extracted. The obtained factors were analyzed using the APCS-MLR model for the apportionment of various pollution sources affecting physicochemical parameters and toxic metals. The results show that the natural soil structure, municipal-industrial wastewater, agricultural-atmospheric runoff, highways, and seasonal effects are the major pollution sources for toxic metals and physicochemical parameters. The material contribution of pollutant sources to toxic metals and physicochemical parameters was calculated and verified by the concentrations analyzed. Consequently, multivariate statistical techniques are useful to determine the physicochemical parameters and toxic metals through reciprocal correlation and assess the seasonal impact of pollutant sources in the basin. This study also provides a basis for the creation of measurement programs, determination of pollution sources, and provision of sustainable watershed management regarding other water resources.

scholarly journals Spatio-Temporal Variation of Groundwater Quality and Source Apportionment Using Multivariate Statistical Techniques for the Hutuo River Alluvial-Pluvial Fan, China

2020 ◽  
Vol 17 (3) ◽  
pp. 1055 ◽  
Author(s):  
Qianqian Zhang ◽  
Long Wang ◽  
Huiwei Wang ◽  
Xi Zhu ◽  
Lijun Wang
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Principal Component ◽  
Domestic Sewage ◽  
Pollution Sources ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Polluted Sites ◽  
Industrial Sewage

Groundwater quality deterioration has become an environmental problem of widespread concern. In this study, we used a water quality index (WQI) and multivariate statistical techniques to assess groundwater quality and to trace pollution sources in the Hutuo River alluvial-pluvial fan, China. Measurement data of 17 variables in 27 monitoring sites from three field surveys were obtained and pretreated. Results showed that there were 53.09% of NO3−, 18.52% of SO42− and 83.95% of total hardness (TH) in samples that exceeded the Grade III standard for groundwater quality in China (GB/T 14848-2017). Based on WQI results, sampling sites were divided into three types: high-polluted sites, medium-polluted sites and low-polluted sites. The spatial variation in groundwater quality revealed that concentrations of total dissolved solids (TDS), Cl−, TH and NO3− were the highest in high-polluted sites, followed by medium-polluted and low-polluted sites. The temporal variation in groundwater quality was controlled by the dilution of rainwater. A principal component analysis (PCA) revealed that the primary pollution sources of groundwater were domestic sewage, industrial sewage and water–rock interactions in the dry season. However, in the rainy and transition seasons, the main pollution sources shifted to domestic sewage and water–rock interactions, nonpoint pollution and industrial sewage. According to the absolute principal component scores-multivariate linear regression (APCS-MLR), most water quality parameters were primarily influenced by domestic sewage. Therefore, in order to prevent the continuous deterioration of groundwater quality, the discharge of domestic sewage in the Hutuo River alluvial-pluvial fan region should be controlled.

scholarly journals Evaluation of the anthropogenic impact in Suat Ugurlu Dam lake using multivariate statistical techniques

2018 ◽  
Vol 20 (1) ◽  
pp. 161-168 ◽  
Keyword(s):  
Anthropogenic Impact ◽  
Sediment Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Cluster Number ◽  
Multivariate Statistical ◽  
Oxidation Reduction ◽  
Oxidation Reduction Potential ◽  
And Cluster Analysis

Sediments play an important role in the quality of aquatic ecosystems in the Dam Lake where they can either be a sink or a source of contaminants, depending on the management. This purpose of this study is to identify the sediment quality in order to find out the causes for the malodor and the eutrophication that is causing a bad scenario. Solutions for improving the dam are proposed. Multivariate statistical techniques, such as a principal component analysis (PCA) and cluster analysis (CA), were applied to the data regarding sediment quality in relation to anthropogenic impact in Suat Ugurlu Dam Lake. This data was generated during 2014-2015, with monitoring at four sites for 11 parameters. A PCA and CA were used in the study of the samples. The total variance of 84.1%, 74.3%, 87.4% and 91.5% suggest 4, 3, 3 and 4 principle components (PCs) in the four locations: LC1, LC2, LC3 and LC4, respectively. Also, a CA was applied to both the variables and the observations. Some variables and observations showed a high similarity based on the results of variables in the CA. Also, the similarity ratio of temperature-mercury (Hg) and oxidation reduction potential (ORP) was high and generally, the cluster number of variables was 5, according to the selected similarity level.

scholarly journals Study of groundwater contamination and drinking suitability in basaltic terrain of Maharashtra, India through PIG and multivariate statistical techniques

2020 ◽  
Vol 69 (4) ◽  
pp. 398-414 ◽  
Author(s):  
Vasant Wagh ◽  
Shrikant Mukate ◽  
Aniket Muley ◽  
Ajaykumar Kadam ◽  
Dipak Panaskar ◽  
...  
Keyword(s):  
Major Ions ◽  
Principal Component ◽  
World Health ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Post Monsoon ◽  
Groundwater Samples

Abstract The integration of pollution index of groundwater (PIG), multivariate statistical techniques including correlation matrix (CM), principal component analysis (PCA), cluster analysis (CA) and various ionic plots was applied to elucidate the influence of natural and anthropogenic inputs on groundwater chemistry and quality of the Kadava river basin. A total of 80 groundwater samples were collected and analysed for major ions during pre- and post-monsoon seasons of 2012. Analytical results inferred that Ca, Mg, Cl, SO4 and NO3 surpass the desirable limit (DL) and permissible limit (PL) of Bureau of Indian Standards (BIS) and the World Health Organization (WHO) in both the seasons. The elevated content of total dissolved solids (TDS), Cl, SO4, Mg, Na and NO3 is influenced by precipitation and agricultural dominance. PIG results inferred that 52.5 and 35%, 30 and 37.5%, 12.5 and 20%, 2.5 and 5% groundwater samples fall in insignificant, low, moderate and high pollution category (PC) in pre- and post-monsoon seasons, respectively. PC 1 confirms salinity controlled process due to high inputs of TDS, Ca, Mg, Na, Cl and SO4. Also, PC 2 suggests alkalinity influence by pH, CO3, HCO3 and F content. PIG and statistical techniques help to interpret the water quality data in an easier way.

scholarly journals Evaluation of the Swat River, Northern Pakistan, water quality using multivariate statistical techniques and water quality index (WQI) model

2020 ◽  
Vol 27 (31) ◽  
pp. 38545-38558 ◽  
Author(s):  
Shah Jehan ◽  
Ihsan Ullah ◽  
Sardar Khan ◽  
Said Muhammad ◽  
Seema Anjum Khattak ◽  
...  
Keyword(s):  
Water Quality ◽  
Health Risk ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Statistical Techniques ◽  
Northern Pakistan ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Poor Water Quality ◽  
Swat River

Abstract This study evaluates the characteristics of water along the Swat River, Northern Pakistan. For this purpose, water samples (n = 30) were collected and analyzed for physicochemical parameters including heavy metals (HM). The mean concentrations of physicochemical parameters and HM were within the drinking water guideline values set by the World Health Organization (WHO 2011) except 34%, 60%, and 56% of copper (Cu), nickel (Ni), and lead (Pb), respectively. Pollution sources were identified by various multivariate statistical techniques including correlation analysis (CA) and principal component analysis (PCA) indicating different origins both naturally and anthropogenically. Results of the water quality index (WQI) ranged from 13.58 to 209 with an average value of 77 suggesting poor water quality for drinking and domestic purposes. The poor water quality was mainly related to high sodium (alkalinity) and salinity hazards showing > 27% and 20% water samples have poor alkalinity and salinity hazards, respectively. Hazard quotient (HQ) and hazard index (HI) were used to determine the health risk of HM in the study area. For water-related health risk, HQingestion, HQdermal, and HI values were > 1, indicating noncarcinogenic health risk (NCR) posed by these HM to the exposed population.

scholarly journals The use of multivariate statistical techniques to establish beach debris pollution sources

2002 ◽  
Vol 36 ◽  
pp. 716-725 ◽  
Author(s):  
D. T. Tudor ◽  
A. T. Williams ◽  
P. Randerson ◽  
A. Ergin ◽  
R. E. Earll
Keyword(s):  
Pollution Sources ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

scholarly journals Classification of artisanal Andalusian cheeses on physicochemical parameters applying multivariate statistical techniques

2015 ◽  
Vol 96 (1) ◽  
pp. 95-106 ◽  
Author(s):  
María Auxiliadora de la Haba Ruiz ◽  
Pilar Ruiz Pérez-Cacho ◽  
Rafaela Dios Palomares ◽  
Hortensia Galán-Soldevilla
Keyword(s):  
Physicochemical Parameters ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

scholarly journals Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2764
Author(s):  
Xiaoxue Ma ◽  
Lachun Wang ◽  
Hong Yang ◽  
Na Li ◽  
Chang Gong
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Pollution Sources ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Wastewater Discharge ◽  
Wet Season ◽  
Multivariate Statistical ◽  
Qinhuai River

Monitoring water quality is indispensable for the identification of threats to water environment and later management of water resources. Accurate monitoring and assessment of water quality have been long-term challenges. In this study, multivariate statistical techniques (MST) and water quality identification index (WQII) were applied to analyze spatiotemporal variation in water quality and determine the major pollution sources in the Qinhuai River, East China. A rotated principal component analysis (PCA) identified three potential pollution sources during the wet season (mixed pollution, physicochemical, and nonpoint sources of nutrients) and the dry season (nutrient, primary environmental, and organic sources) and they explained 81.14% of the total variances in the wet season and 78.42% of total variances in the dry season. The result of redundancy analysis (RDA) showed that population density, urbanization, and wastewater discharge are the main sources of organic pollution, while agricultural fertilizer consumption and industrial wastewater discharge are the main sources of nutrients such as nitrogen and phosphorus. The water quality of the Qinhuai River basin was determined to be mainly Class III (slightly polluted) and Class IV (moderately polluted) based on WQII. Temporally, the change trend of WQII showed that water quality gradually deteriorated between 1990 and 2005, improved between 2006 and 2010, and then deteriorated again. Spatially, the WQII distribution map showed that areas with more developed urbanization were relatively more polluted. Our results show that MST and WQII are useful tools to help the public and decision makers to evaluate the water quality of aquatic environment.

scholarly journals Water Quality Assessment of Porsuk River, Turkey

2010 ◽  
Vol 7 (2) ◽  
pp. 593-599 ◽  
Author(s):  
Suheyla Yerel
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Water ◽  
Surface Water Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Porsuk River

The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management.

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